Department Publications

Deployment of unmanned aerial vehicles (UAVs) as aerial base stations (ABSs) has been considered to be a feasible solution to provide network coverage in scenarios where the conventional terrestrial network is overloaded or inaccessible due to an emergency situation. This article studies the problem of optimal placement of the UAVs as ABSs to enable network connectivity for the users in such a scenario. The main contributions of this work include a less complex approach to optimally position the UAVs and to assign user equipment (UE) to each ABS, such that the total spectral efficiency (TSE) of the network is maximized, while maintaining a minimum QoS requirement for the UEs. The main advantage of the proposed approach is that it only requires the knowledge of UE and ABS locations and statistical channel state information. The optimal 2-dimensional (2D) positions of the ABSs and the UE assignments are found using K-means clustering and a stable marriage approach, considering the characteristics of the air-to-ground propagation channels, the impact of co-channel interference from other ABSs, and the energy constraints of the ABSs. Two approaches are proposed to find the optimal altitudes of the ABSs, using search space constrained exhaustive search and particle swarm optimization (PSO). The numerical results show that the PSO-based approach results in higher TSE compared to the exhaustive search-based approach in dense networks, consuming similar amount of energy for ABS movements. Both approaches lead up to approximately 8-fold energy savings compared to ABS placement using naive exhaustive search.

Neural processing along the ascending auditory pathway is often associated with a progressive reduction in characteristic processing rates. For instance, the well-known frequency-following response (FFR) of the auditory midbrain, as measured with electroencephalography (EEG), is dominated by frequencies from ∼100 Hz to several hundred Hz, phase-locking to the acoustic stimulus at those frequencies. In contrast, cortical responses, whether measured by EEG or magnetoencephalography (MEG), are typically characterized by frequencies of a few Hz to a few tens of Hz, time-locking to acoustic envelope features. In this study we investigated a crossover case, cortically generated responses time-locked to continuous speech features at FFR-like rates. Using MEG, we analyzed responses in the high gamma range of 70–200 Hz to continuous speech using neural source-localized reverse correlation and the …

Vehicle-to-vehicle (V2V) communication plays a pivotal role in intelligent transport systems (ITS) with cellular-vehicle to everything (C-V2X) and IEEE 802.11p being the two competing enabling technologies. This paper presents multi-dimensional discrete-time Markov chain (DTMC) based models to study the medium access control (MAC) layer performance of the IEEE 802.11p standard and C-V2X Mode 4, considering periodic cooperative awareness messages (CAMs) and event-driven decentralized environmental notification messages (DENMs). Closed-form solutions for the steady-state probabilities of the models are obtained, which are then utilized to derive expressions for several key performance metrics. Numerical results are provided to draw insights on the performance. In particular, a performance comparison between IEEE 802.11p and C-V2X Mode 4 in terms of the average delay, the collision …

This paper discusses advances related to the integration of future mobile electronic THz systems. Without claiming to provide a comprehensive review of this surging research area, the authors gathered research on selected topics that are expected to be of relevance for the future exploration of components for practical mobile THz imaging and sensing applications. First, a brief technology review of integrated mobile THz components is given. Advances in III-V technology, silicon technology, and resonant-tunneling diodes (RTD) are discussed. Based on an RTD source and a SiGe-HBT direct detector, low-cost and compact computed tomography is presented for volumetric continuous-wave imaging at around 300 GHz. Moreover, aspects of system integration of mobile THz MIMO radars are discussed. Thereby, a novel phase-locked loop concept utilizing a high-stability yttrium-iron-garnet-tuned oscillator to …

Geometric information of scenes available with four-dimensional (4-D) light fields (LFs) paves the way for post-capture refocusing. Light field refocusing methods proposed so far have been limited to a single planar or a volumetric region of a scene. In this letter, we demonstrate simultaneous refocusing of multiple volumetric regions in LFs. To this end, we employ a 4-D sparse finite-extent impulse response (FIR) filter consisting of multiple hyperfan-shaped passbands. We design the 4-D sparse FIR filter as an optimal filter in the least-squares sense. Experimental results confirm that the proposed filter provides 63% average reduction in computational complexity with negligible degradation in the fidelity of multi-volumetric refocused LFs compared to a 4-D nonsparse FIR filter.

Low-complexity signal processing algorithms and reconfigurable digital hardware architectures are proposed for multi-depth filtering and occlusion suppression in 4-D light fields (LFs). The proposed multi-depth-pass (MDP) and multi-depth-reject (MDR) 4-D filters allow simultaneous enhancement of planar objects at multiple desired depths and attenuation of planar objects at multiple undesired depths, thereby achieving electronically tunable multi-depth focusing. Partial-separability and recursive nature of the proposed MDP and MDR transfer functions ensure ultra-low hardware complexity suitable for real-time processing of large volume of input samples encountered in 4-D LFs. Filter synthesis details and examples with synthetic and real LFs of size 15  ×  15 × 434  ×  625 are presented. A generic framework encapsulating a multitude of possible digital hardware realizations is described, employing J …

This paper studies optimum detectors and error rate analysis for wireless systems with low-resolution quantizers in the presence of fading and noise. A universal lower bound on the average symbol error probability (SEP), correct for all M-ary modulation schemes, is obtained when the number of quantization bits is not enough to resolve M signal points. In the special case of M-ary phase shift keying (M-PSK), the maximum likelihood detector is derived. Utilizing the structure of the derived detector, a general average SEP expression for M-PSK modulation with n-bit quantization is obtained when the wireless channel is subject to fading with a circularly-symmetric distribution. For the Nakagami-m fading, it is shown that a transceiver architecture with n-bit quantization is asymptotically optimum in terms of communication reliability if n ≥ log 2 M + 1. That is, the decay exponent for the average SEP is the same and equal …

Stroke patients with small central nervous system infarcts often demonstrate an acute dysexecutive syndrome characterized by difficulty with attention, concentration, and processing speed, independent of lesion size or location. We use magnetoencephalography (MEG) to show that disruption of network dynamics may be responsible. Nine patients with recent minor strokes and eight age-similar controls underwent cognitive screening using the Montreal cognitive assessment (MoCA) and MEG to evaluate differences in cerebral activation patterns. During MEG, subjects participated in a visual picture–word matching task. Task complexity was increased as testing progressed. Cluster-based permutation tests determined differences in activation patterns within the visual cortex, fusiform gyrus, and lateral temporal lobe. At visit 1, MoCA scores were significantly lower for patients than controls (median [interquartile range …

Four-dimensional (4-D) light fields (LFs) enable novel imaging technologies, which are traditionally based on two-dimensional images. In most of these applications, denoising of LFs is required as a preprocessing technique before any subsequent processing. We propose a real-time LF denoising method using a novel 4-D linear and shift-invariant hyperfan filter. The proposed method exploits sparsity of the spectrum of a LF and the 4-D hyperfan filter is implemented in the 4-D mixed-domain (i.e.,two-dimensional space and two-dimensional frequency) leading to significant reductions in computational and memory complexities. A software implementation of the proposed method provides better or comparable denoising performance for grayscale and color LFs with respect to the metrics peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM) compared to previously reported linear LF denoising methods, while …

The increased popularity of investigations and exploits in the fields of neurological rehabilitation, human emotion recognition, and other relevant brain-computer interfaces demand the need for flexible electrophysiology data acquisition systems. Such systems often require to be multi-modal and multi-channel capable of acquiring and processing several different types of physiological signals simultaneously in realtime. Developments of modular and scalable electrophysiological data acquisition systems for experimental research enhance understanding and progress in the field. To contribute to such an endeavor, we present an open-source hardware project called High-Channel Count Electrophysiology or HiCCE, targeting to produce an easily-adaptable, cost-effective, and affordable electrophysiological acquisition system as an alternative solution for mostly available commercial tools and the current state of the …

Growing concerns over privacy invasion due to video camera based monitoring systems have made way to non-invasive Wi-Fi signal sensing based alternatives. This paper introduces a novel end-to-end deep learning framework that utilizes the changes in orthogonal frequency division multiplexing (OFDM) sub-carrier amplitude information to simultaneously predict the identity, activity and the trajectory of a user and create a user profile that is of similar utility to a one made through a video camera based approach. The novelty of the proposed solution is that the system is fully autonomous and requires zero user intervention unlike systems that require user originated initialization, or a user held transmitting device to facilitate the prediction. Experimental results demonstrate over 95% accuracy for user identification and activity recognition, while the user localization results exhibit a ±12cm error, which is a significant …

This paper describes a technique for de-embedding electromagnetic mutual coupling effects between nearest neighbors of a ULA receiver. Measured S-parameters across a range of frequencies of interest are used in a closed-form mathematical model that relates measured S-parameters, measured LNA reflection coefficients, and transmission line parameters to digital beamformer design equations, such that the beam shape distortions arising from mutual coupling can be compensated in the DSP algorithm. A 5.8 GHz 32-element receiver array with custom receivers and 32-channel Xilinx Virtex-6 Sx35 FPGA based DSP system implementing a real-time complex-valued Frost beamformer operating at IF is proposed to show the benefits of de-embedding mutual coupling for far-field side-lobe suppression. The worst-case side-lobe level is reduced by 11.2 dB, and the average side-lobe level is reduced by 5.2 dB, for …

Intraoperative PET-laparoscope system has been proposed to locate lymph node metastasis during gastric cancer surgery. The proposed system consists of an external detector array connected to a patient’s surgical bed and a movable detector probe which is inserted into the patient’s stomach along with a laparoscope. In this study a prototype detector for the PET-laparoscope system is presented with a pixelated movable detector probe tracked by an optical tracking system. Detector prototype reconstructed images of a 22 Na source with 3 mm spatial resolution in X (coronal) and Z (longitudinal) directions in a 2 min scan. The use of the pixelated multi-layer movable detector probe improved the spatial resolution of the system.

This research paper presents a novel track based stair-climbing wheelchair design with improved stability and performance. The proposed design has an adaptable arrangement to the staircase especially at the top of the staircase. In the existing wheelchair designs, this particular feature has not been adequately addressed. The proposed design consists of body frame and supporting rubber track arms. Of the two main operations; ascending and descending, the most critical operations is at the top of the staircase, which is smoothly controlled by following the staircase profile. This motion is achieved by properly synchronizing the rubber track arms according to a pre-planned motion profile. As a result, user does not feel sudden motions in position and attitude. Furthermore, the proposed wheelchair is adaptable to any staircase.

Network softwarization enables the novel concept of Local 5G Operator (L5GO) networks, for deploying localized 5G solutions to serve case and location specific communication needs. This paper proposes a L5GO network architecture for delay critical future telehealth services, considering two use cases on augmented reality assisted and robotic aided surgery. The paper compares the latency performance of the proposed L5GO architecture with a traditional legacy network and a network equipped with Multi-access Edge Computing (MEC). The results highlight the unique advantages of utilizing an L5GO to cater the communication needs of delay critical telehealth, compared to a traditional network.

We propose self-supervised deep algorithms to detect anomalies in heterogeneous autonomous systems using frontal camera video and IMU readings. Given that the video and IMU data are not synchronized, each of them are analyzed separately. The vision-based system, which utilizes a conditional GAN, analyzes immediate-past three frames and attempts to predict the next frame. The frame is classified as either an anomalous case or a normal case based on the degree of difference estimated using the prediction error and a threshold. The IMU-based system utilizes two approaches to classify the timestamps; the first being an LSTM autoencoder which reconstructs three consecutive IMU vectors and the second being an LSTM forecaster which is utilized to predict the next vector using the previous three IMU vectors. Based on the reconstruction error, the prediction error, and a threshold, the timestamp is classified as either an anomalous case or a normal case. The composition of algorithms won runners up at the IEEE Signal Processing Cup anomaly detection challenge 2020. In the competition dataset of camera frames consisting of both normal and anomalous cases, we achieve a test accuracy of 94% and an F1-score of 0.95. Furthermore, we achieve an accuracy of 100% on a test set containing normal IMU data, and an F1-score of 0.98 on the test set of abnormal IMU data.

Getting a team of robots to achieve a relatively complex task using manual manipulation through augmented reality (AR) is interesting. However, the true potential of such an approach manifests when the system can learn from humans. We propose a system comprising a team of robots that performs a previously unseen task—a variant, to be specific—by learning from the sequences of actions taken by multiple human beings doing this task in various ways using deep learning (DL). The training inputs can be through actual manipulation of the team of robots using an augmented-reality tablet or through a simulator. Results indicate that the system is able to fulfill the specified variant of the task more than 80% of the time, inaccuracies mainly owing to unrealistic specifications of tasks. This opens up an avenue of training a team of robots, instead of crafting a rule base.

The potential of drone based services is enormous with applications ranging from consumer product delivery to health services. When meeting this demand, one of the major challenges we face is the noise produced by drones, which not only contributes to listener discomfort, but also hinders the device's ability to effectively communicate via audio. Thus, there exists a pressing need for understanding the characteristics of drone related noise, which can then be suppressed using suitable methods. This paper presents a preliminary study on modeling the relationship between input motor current and acoustic noise produced by a drone. An experimental study is conducted indoors for a drone under hovering manoeuvre with a single active motor and propeller. The drone noise was measured by a single on-board microphone. We identify multiple tones or harmonics in the drone noise spectrum that vary proportionally …

A low-complexity codec and a hardware architecture are proposed for achieving real-time compression of four-dimensional (4-D) light field (LF) signals captured from camera/lenslet arrays. The proposed system employs the 4-D extension of the two-dimensional (2-D) 8×8 approximate discrete cosine transform (ADCT) that has recently appeared in the literature. Motivated by the partial separability of the multidimensional spectrum of LFs, the proposed 4-D ADCT is obtained by cascading 2-D inter-view and 2-D intra-view transform stages. Software simulations are provided to confirm the performance of the 4-D ADCT based compression and comparisons are made with respect to 2-D inter-view only and 2-D intra-view only ADCT-based compression. Proposed digital architectures are validated using stepped hardware co-simulation on a Xilinx Virtex-7 VC-707 FPGA platform verifying 597 MHz maximum possible …

The emergence of Industry 4.0 has resulted in a rapid increase in the demand for bandwidth due to the proliferation of the industrial Internet of Things (IIoT). Increased utilization of the network also gives rise to undesirable consequences such as high latency and increased likelihood of data loss through packet drops. Encoding inputs using event-based sampling is a potential solution for decreasing network traffic generated when continuous input variables are sampled. The problem addressed by this article is the fact that currently there is no effective method for comparing different encoders. The resulting contribution is the use of mutual information to compare the input and encoded output in terms of accuracy for the currently most efficient encoders that use either memory based event triggering (MBET) or deadband error modulation (DEM). This allows a practitioner to select a suitable encoder for a given input …

We present a novel low cost time-of-flight (ToF) sensor deck for micro UAVs capable of sparse range measurements for localisation and mapping. In disaster scenarios such as building collapses, the larger UAVs equipped with highfidelity sensors such as lidars, structured light cameras and stereo cameras to perceive the environment with high resolution, may have difficulties when navigating through cluttered confined spaces. Micro UAVs are ideal for providing vital data in the aftermath of a disaster scenario but does not have the payload capacity for such sensors. Miniature low cost ToF sensors can provide range measurements albeit with low range and resolution. The proposed solution includes the design and implementation of a novel sensing deck for a commercially available micro UAV with 13 miniature ToF sensors, a low resolution thermal camera and a method to generate reliable 2D maps of GPS denied areas. The sparse observations obtained from range sensors are combined in a Rao-Blackwellized particle filter to create a reliable map. The ToF sensors are arranged to capture a maximum number of points in the horizontal plane at the flying height despite fluctuations in altitude and orientation. Particle weighting and resampling are only performed in a loop closure or when moving through a known area. Importance weight for the particles are computed by an Iterative Closest Point based method by calculating the error between nearest scan points. The proposed method has been tested in indoor environments and verified as a robust method to generate reliable maps of unknown environments.

We consider the problem of determining Granger causal influences among sources that are indirectly observed through low-dimensional and noisy linear projections. Commonly used methods proceed in a two-stage fashion, by first solving an inverse problem to localize the sources, and then inferring the Granger causal influences from the estimated sources. The inferred Granger causal links thus inherit the various biases that are used in source localization techniques, in the form of spatiotemporal priors designed in favor of spatial localization. In addition, this approach does not account for the structural properties of the underlying functional networks such as sparsity of the links. We address these issues by modeling the source dynamics as sparse vector autoregressive processes and estimate the model parameters directly from the observations, with no recourse to intermediate source localization. We evaluate …

Estimating parameters and properties of various materials without causing damage to the material under test (MUT) is important in many applications. Thus, in this letter, we address MUT's parameter estimation by wireless sensing. Here, the precision of the estimation depends on the accurate estimation of the properties of the reflected signal from the MUT (e.g., number of reflections, their amplitudes, and time delays). For a layered MUT, there are multiple reflections, and due to the limited bandwidth at the receiver, these reflections superimpose with each other. Since the number of reflections coming from the MUT is limited, we utilize sparse signal processing (SSP) to decompose the reflected signal. In SSP, a so called dictionary is required to obtain a sparse representation of the signal. Here, instead of a fixed dictionary, an iterative dictionary-update technique is proposed to improve the estimation of the reflected …

This research paper presents a proof of concept for real-time teleoperation of a robotic arm via internet consisting features such as tactile sense, haptic and visual feedback. According to our system model, a realistic operational experience could be provided to the robotic arm operator when operating the robotic arm to perform tasks in a remote location. Two different algorithms were developed for master and slave ends. Force resistive sensors were used for tactile sensing. Vibrating wearable was designed for haptic feedback. IP camera was used to provide real time visual feedback. The integrated real time robotic control system operates with tactile sensing range of 0.05 N to 0.1 N, overall system response delay of 7.4 ms and accuracy of 99%.

In this paper, a vision-based adaptive traffic light controller is proposed. The proposed controller was successfully deployed and tested as a complete system in a complex roundabout in Colombo city at a highly congested time. There were two main parts to this implementation. The first part was a vision-based traffic monitoring system. In this part, a system was developed so that it monitored lanes in a junction with cameras and extracted a traffic index based on traffic density, vehicle type, and pixel-wise velocity of vehicles by processing the video streams coming from cameras. The traffic signal light controlling part was the second part of the project. This part dealt with estimating a better timing adjustment for the existing system using a mathematical modeling approach while taking the extracted traffic index as input. This system was operated with the existing system with minimum alterations for easy real-world …